class_docstr: Stacking for binary classification.
  
  Parameters
  ----------
  models
  meta_classifier
  include_features
      Indicates whether or not the original features should be provided to the meta-model along
      with the predictions from each model.
  
  Examples
  --------
  
  >>> from river import compose
  >>> from river import datasets
  >>> from river import ensemble
  >>> from river import evaluate
  >>> from river import linear_model as lm
  >>> from river import metrics
  >>> from river import preprocessing as pp
  
  >>> dataset = datasets.Phishing()
  
  >>> model = compose.Pipeline(
  ...     ('scale', pp.StandardScaler()),
  ...     ('stack', ensemble.StackingClassifier(
  ...         [
  ...             lm.LogisticRegression(),
  ...             lm.PAClassifier(mode=1, C=0.01),
  ...             lm.PAClassifier(mode=2, C=0.01),
  ...         ],
  ...         meta_classifier=lm.LogisticRegression()
  ...     ))
  ... )
  
  >>> metric = metrics.F1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  F1: 88.14%
  
  References
  ----------
  [^1]: [A Kaggler's Guide to Model Stacking in Practice](http://blog.kaggle.com/2016/12/27/a-kagglers-guide-to-model-stacking-in-practice/)
code: def predict_proba_one(self, x):
      oof = {
          f"oof_{i}_{k}": p
          for i, clf in enumerate(self)
          for k, p in clf.predict_proba_one(x).items()
      }
  
      if self.include_features:
          oof.update(x)
  
      return self.meta_classifier.predict_proba_one(oof)
  
file_path: river/river/ensemble/stacking.py
imports: from __future__ import annotations
  from river import base, linear_model, naive_bayes, tree
label_desc: Use image node_id 4 for calling the StackingClassifier obj's underlying member method code with example usage: obj.predict_proba_one(x) and returns: self